Novel Materials

Pillar 1 — Design and Discovery of Novel Materials. This pillar is dedicated to two phase II projects that have shown significant scientific and economic impact — metal-organic frameworks and alloys for additive manufacturing.

Recent developments in materials science and chemistry have created a problem of luxury: we can conceive of many more materials than we can ever imagine synthesizing and testing. Finding the optimal material for any particular application has therefore become increasingly difficult.

We think that identifying the highest-performing materials for a specific application will likely be achieved only by the adroit combination of high-throughput first-principles computations, theory and simulation, and machine learning, guiding experimental teams towards the most promising materials. To this end, we are building on the experience gained in MARVEL's phases I and II to develop a general framework for guiding experimental efforts in material discovery and creation. We are developing this framework in the areas of nanoporous materials (e.g., metal-organic frameworks (MOFs), covalent organic frameworks (COFs), zeolites, etc.) and multicomponent metal alloys (traditional dilute and, especially, new high-entropy alloys). Both topics have immense promise for applications but pose the challenge of a vast composition space. The computational methods developed in phase III will therefore have a significant impact.

The project is led by William Curtin and Berend Smit.

Group Leaders

Berend Smit

Berend Smit

Project leader

EPFL, Sion

William Curtin

William Curtin

Project leader

EPFL, Lausanne

Vladyslav Turlo

Vladyslav Turlo

Group leader

Empa, Thun

Anirudh Raju Natarajan

Anirudh Raju Natarajan

Group leader

EPFL, Lausanne

Christian Leinenbach

Christian Leinenbach

Group leader

Empa, Thun

Michele Ceriotti

Michele Ceriotti

Deputy director

EPFL, Lausanne

View people involved

Scheme of the workflow that starts with the uploading of the CIF structure of a crystalline material, the curation of its crystal geometry, the computation of pore geometry, thermodynamic and transport properties, and final its performance evaluation and ranking for a specific application.

Related publications (until January 2024)

K. M. Jablonka, P. Schwaller, A. Ortega-Guerrero, B. Smit, Leveraging large language models for predictive chemistry, Nature Machine Intelligence 6, 161–169 (2024). [Open Access URL]
Dataset on GitHub.
Group(s): Smit / Project(s): P1
X. Liu, W. A. Curtin, Mechanism of thermally-activated prismatic slip in Mg, Acta Materialia 262, 119402 (2024). [Open Access URL]
Dataset on Materials Cloud.
Group(s): Curtin / Project(s): P1
S. Nag, W. A. Curtin, Solute-strengthening in metal alloys with short-range order, Acta Materialia 263, 119472 (2024).[Open Access URL]
Group(s): Curtin / Project(s): P1
X. Liu, W. A. Curtin, Atomistic simulations reveal strength reductions due to short-range order in alloys, Acta Materialia 263, 119471 (2024). [Open Access URL]
Dataset on Materials Cloud.
Group(s): Curtin / Project(s): P1
V. Eyert, J. Wormald, W. A. Curtin, E. Wimmer, Machine-learned interatomic potentials: Recent developments and prospective applications, Journal of Materials Research 38, 5079 (2023). [Open Access URL]
Group(s): Curtin / Project(s): P1
H. Gustafsson, M. Kozdra, B. Smit, S. Barthel, A. Mace, Predicting Ion Diffusion from the Shape of Potential Energy Landscapes, Journal of Chemical Theory and Computation 20, 18–29 (2023). [Open Access URL]
Group(s): Smit / Project(s): P1
B. Yin, L. Li, S. Drescher, S. Seils, S. Nag, J. Freudenberger, W. A. Curtin, Solute misfit and solute interaction effects on strengthening: A case study in AuNi, Acta Materialia 257, 119118 (2023). [Open Access URL]
Dataset on Materials Cloud.
Group(s): Curtin / Project(s): P1
M. R. Niazi, W. A. Curtin, Solute strengthening of edge prism dislocations in Mg alloys, European Journal of Mechanics - A/Solids (2023). [Open Access URL]
Dataset on Materials Cloud.
Group(s): Curtin / Project(s): P1
J. F. Troncoso, Y. Hu, N. M. della Ventura, A. Sharma, X. Maeder, V. Turlo, Machine learning of twin/matrix interfaces from local stress field, Computational Materials Science 228, 112322 (2023). [Open Access URL]
Dataset on Materials Cloud.
Group(s): Turlo / Project(s): P1
S. Gramatte, L. P. H. Jeurgens, O. Politano, J. A. Simon Greminger, F. Baras, A. Xomalis, V. Turlo, Atomistic Simulations of the Crystalline-to-Amorphous Transformation of γ–Al_2O_3 Nanoparticles: Delicate Interplay between Lattice Distortions, Stresses, and Space Charges, Langmuir 39, 6301–6315 (2023). [Open Access URL]
Group(s): Turlo / Project(s): P1
G. Winkens, A. Kauffmann, J. Herrmann, A. K. Czerny, S. Obert, S. Seils, T. Boll, C. Baruffi, Y. Rao, W. A. Curtin, R. Schwaiger, M. Heilmaier, The influence of lattice misfit on screw and edge dislocation-controlled solid solution strengthening in Mo-Ti alloys, Communications Materials 4, 26 (2023). [Open Access URL]
Dataset on KITopen.
Group(s): Curtin / Project(s): P1
A. C. P. Jain, M. Ceriotti, W. A. Curtin, Natural aging and vacancy trapping in Al-6xxx, Journal of Materials Research 38, 5171–5187 (2023). [Open Access URL]
Group(s): Ceriotti, Curtin / Project(s): P1, P2
P. Gäumann, T. Rohrbach, L. Artiglia, D. Ongari, B. Smit, J. A. van Bokhoven, M. Ranocchiari, Tandem Hydroformylation-Aldol Condensation Reaction Enabled by Zn-MOF-74, Chemistry–A European Journal 29, e202300939 (2023). [Open Access URL]
Group(s): Smit / Project(s): P1
K. M. Jablonka, Q. Ai, A. Al-Feghali, S. Badhwar, J. D. Bocarsly, A. M. Bran, S. Bringuier, L. C. Brinson, K. Choudhary, D. Circi, S. Cox, W. A. de Jong, M. L. Evans, N. Gastellu, J. Genzling, M. V. Gil, A. K. Gupta, Z. Hong, A. Imran, S. Kruschwitz, A. Labarre, J. Lála, T. Liu, S. Ma, S. Majumdar, G. W. Merz, N. Moitessier, E. Moubarak, B. Mouriño, B. Pelkie, M. Pieler, M. C. Ramos, B. Ranković, S. G. Rodriques, J. N. Sanders, P. Schwaller, M. Schwarting, J. Shi, B. Smit, B. E. Smith, J. Van Herck, C. Völker, L. Ward, S. Warren, B. Weiser, S. Zhang, X. Zhang, G. A. Zia, A. Scourtas, K. J. Schmidt, I. Foster, A. D. White, B. Blaiszik, 14 examples of how LLMs can transform materials science and chemistry: a reflection on a large language model hackathon, Digital Discovery 2, 1233–1250 (2023). [Open Access URL]
Group(s): Smit / Project(s): P1
B. Mourino, K. M. Jablonka, A. Ortega-Guerrero, B. Smit, In Search of Covalent Organic Framework Photocatalysts: A DFT-Based Screening Approach, Advanced Functional Materials 33, 2301594 (2023). [Open Access URL]
Dataset on Zenodo.
Group(s): Smit / Project(s): P1
C. Baruffi, F. Maresca, W. A. Curtin, Screw vs. edge dislocation strengthening in body-centered-cubic high entropy alloys and implications for guided alloy design, MRS Communications 12, 1111 (2022). [Open Access URL]
Group(s): Curtin / Project(s): P1
J. F. Troncoso, G. Lorenzin, C. Cancellieri, V. Turlo, Explaining the Effect of In-Plane Strain on Thermal Degradation Kinetics of Cu/W Nano-Multilayers, Acta Materialia, Inc. First Look (2023). [Open Access URL]
Dataset on Materials Cloud.
Group(s): Turlo / Project(s): P1

N. P. Domingues, S. M. Moosavi, L. Talirz, K. M. Jablonka, C. P. Ireland, F. M. Ebrahim, B. Smit, Using genetic algorithms to systematically improve the synthesis conditions of Al-PMOF, Communications Chemistry 5, 170 (2022). [Open Access URL]
Dataset on Zenodo.
Group(s): Smit / Project(s): P1
Y. Hu, W. A. Curtin, Modeling of precipitate strengthening with near-chemical accuracy: case study of Al-6xxx alloys, Acta Materialia 237, 118144 (2022). [Open Access URL]
Dataset on Materials Cloud.
Group(s): Curtin / Project(s): P1
X. Liu, M. R. Niazi, T. Liu, B. Yin, W. A. Curtin, A low-temperature prismatic slip instability in Mg understood using machine learning potentials, Acta Materialia 243, 118490 (2023). [Open Access URL]
Dataset on Materials Cloud.
Group(s): Curtin / Project(s): P1
M. Liyanage, D. Reith, V. Eyert, W. A. Curtin, Machine learning for metallurgy V: A neural-network potential for zirconium, Physical Review Materials 6, 063804 (2022). [Open Access URL]
Dataset on Materials Cloud.
Group(s): Curtin / Project(s): P1
D. Ongari, L. Talirz, K. M. Jablonka, D. W. Siderius, B. Smit, Data-Driven Matching of Experimental Crystal Structures and Gas Adsorption Isotherms of Metal-Organic Frameworks, Journal of Chemical & Engineering Data 67, 1743–1756 (2022). [Open Access URL]
Dataset on GitHub.
Group(s): Smit / Project(s): P1
Y. Rao, C. Baruffi, A. D. Luca, C. Leinenbach, W. A. Curtin, Theory-guided design of high-strength, high-melting point, ductile, low-density, single-phase BCC high entropy alloys, Acta Materialia 237, 118132 (2022). [Open Access URL]
Dataset on Materials Cloud.
Group(s): Curtin / Project(s): P1